New Study on Data Scientist Salaries (2014)

Recent internet search trends and social media buzz prove beyond a doubt that big data careers specifically those in data science, are closely followed. As data storage technologies become inexpensive, and as the non-traditional channels of data collection like social media and industrial internet increase, the need for data scientists to capture, store, and analyze the terabytes and petabytes of unstructured data increases as well. Businesses are now capable of achieving more thanks to the meteoric rise of data science. Now market research companies and talent acquisition firms are targeting data scientists offering professional wages and associate job responsibilities.

In most cases, businesses large, medium, or small, are stuck with nothing but a mere suspicion that the untapped data might provide competitive advantages, but this suspicion is enough to trigger a widespread interest in this burgeoning field. Its popularly believed that data scientists have the magic potion for extracting goldmines of insights from large volumes of unstructured data.

A US-based executive search firm known as Burtch Works conducted a study on the job responsibilities of data scientists and corresponding salaries.

Purpose of Burtch Works Study: Salaries of Data Scientists

The purpose of this report is to provide current information on the compensation of data scientists. In their earlier study in 2013, Burtch Works intentionally left out the data scientists from the ambit of their research because, in their opinion, the nature and role of data scientists warrant a separate study.

The Burtch Works Study: Salaries of Data Scientists, published April 2014, tries to establish that although like other data professionals, data scientists often employ quantitative skills to gain business insights, they usually work with enormous sets of unstructured data. Thus, apart from possessing data analytics skills, these highly sought after data scientists also possess the necessary skills to capture, store and retrieve high volumes of unstructured data.

Identifying data scientist roles

Burtch Works used a combination of academic backgrounds, job responsibilities, and acquired skill sets to determine who exactly fit the bill of data scientist as a formal mechanism for identifying this role was nonexistent.

Tthey found out that data scientists usually have a masters or doctoral degree in a quantitative field like math, computer science, or engineering. Data scientists are, in general, adept at using tools like Hadoop or MapReduce to store data, or Pig and Hive to retrieve data. They display an in-depth knowledge of statistical methods for deriving insights from data. Almost unanimously, the job responsibilities of data scientists require them to interact with large volumes of unstructured datasets.

Study method used to collect compensation data

The types of data used for this study included gender, residency, job location, industry sector, educational background, job level, job responsibilities, base salary, compensatory bonuses, and managerial responsibilities, if any. Burtch Works conducted interviews to collect the complete data for each respondent.

This direct interviewing method provided the rare opportunity for obtaining information that is not commonly shared by HR departments, such as education history or residency status.

For the purpose of the study, the data scientists were categorized in six levelsthree employee levels and three managerial levels. The study helps to establish how the monetary compensation varied by category, by demographics, by years of experience, etc. The comparison chart of compensation of data scientists and other big data professionals clearly show that data scientists are leading the compensation package!

Source: The Burtch Works Study: Salaries of Data Scientists

Findings of the study

The intensive study conducted and reported by Burtch Works contains the following findings:

Data science is a young field, with average years of experience among professionals being nine years.

In general, data scientists are paid more than their counterparts in the big data profession, the technology and gaming industry sectors being the highest payers.

The gender gap is acute with only 12 % representing women data scientists.

More than three-quarters of the professionals possess a Masters degree or a PhD in a quantitative field. Although mathematics is the desired academic background, many candidates from science, engineering, or social sciences find inroads into this field.

Currently, technology industry is the biggest employer of data scientists.

In the west coast and northeast, data scientists at levels 1 and 2 are paid more by firms. However, level 3 data scientists are paid uniformly throughout US.

On the US map, a disproportionately high percentage of data scientists (43%) work on the west coast, many of whom are foreign-born.

More than a third of all US-based data scientists are foreigners sponsored by their employers.

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